About: Relaxation Heuristics for Multiagent Planning

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://www.aaai.org/ocs/index.php/ICAPS/ICAPS14/paper/view/7930
Description	Similarly to classical planning, in MA-Strips multiagent planning, heuristics significantly improve efficiency of search-based planners. Heuristics based on solving a relaxation of the original planning problem are intensively studied and well understood. In particular, frequently used is the delete relaxation, where all delete effects of actions are omitted. In this paper, we present a unified view on distribution of delete relaxation heuristics for multiagent planning. Until recently, the most common approach to adaptation of heuristics for multiagent planning was to compute the heuristic estimate using only a projection of the problem for a single agent. In this paper, we place such approach in the context of techniques which allow sharing more information among the agents and thus improve the heuristic estimates. We thoroughly experimentally evaluate properties of our distribution of additive, max and Fast-Forward relaxation heuristics in a planner based on distributed Best-First Search. The best performing distributed relaxation heuristics favorably compares to a state-of-the-art MA-Strips planner in terms of benchmark problem coverage. Finally, we analyze impact of limited agent interactions by means of recursion depth of the heuristic estimates. Similarly to classical planning, in MA-Strips multiagent planning, heuristics significantly improve efficiency of search-based planners. Heuristics based on solving a relaxation of the original planning problem are intensively studied and well understood. In particular, frequently used is the delete relaxation, where all delete effects of actions are omitted. In this paper, we present a unified view on distribution of delete relaxation heuristics for multiagent planning. Until recently, the most common approach to adaptation of heuristics for multiagent planning was to compute the heuristic estimate using only a projection of the problem for a single agent. In this paper, we place such approach in the context of techniques which allow sharing more information among the agents and thus improve the heuristic estimates. We thoroughly experimentally evaluate properties of our distribution of additive, max and Fast-Forward relaxation heuristics in a planner based on distributed Best-First Search. The best performing distributed relaxation heuristics favorably compares to a state-of-the-art MA-Strips planner in terms of benchmark problem coverage. Finally, we analyze impact of limited agent interactions by means of recursion depth of the heuristic estimates. (en)
Title	Relaxation Heuristics for Multiagent Planning Relaxation Heuristics for Multiagent Planning (en)
skos:prefLabel	Relaxation Heuristics for Multiagent Planning Relaxation Heuristics for Multiagent Planning (en)
skos:notation	RIV/68407700:21230/14:00224542!RIV15-GA0-21230___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(GA13-22125S)
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Štolba, Michal
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	42080
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/14:00224542
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	automated planning; multiagent planning; heuristics (en)
http://linked.open.../riv/klicoveSlovo	multiagent planning automated planning heuristics
http://linked.open...ontrolniKodProRIV	[2F7A3CB72528]
http://linked.open...v/mistoKonaniAkce	Portsmouth, NH
http://linked.open...i/riv/mistoVydani	Menlo Park, California
http://linked.open...i/riv/nazevZdroje	Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Deterministic Domain-independent Multi-agent Planning
http://linked.open...UplatneniVysledku	2014
http://linked.open...iv/tvurceVysledku	Komenda, Antonín Štolba, Michal
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2014-06-21 (xsd:date)
number of pages	9 (xsd:int)
http://purl.org/ne...btex#hasPublisher	AAAI Press
https://schema.org/isbn	978-1-57735-660-8
http://localhost/t...ganizacniJednotka	21230

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